Developmental Stages

The analysis of the different developmental stages showed that in all stages a similar amount of around 2670 transcription factors is expressed. In the cluster analysis, three

6.3 TRANSCRIPTION FACTOR STUDY 113

main clusters were detectable (Chapter 5.3.5. The very early stages (2-cell, 30%- epiboly) formed a cluster as well as the middle embryonic stages (1-6 somites, 24hpf and 48hpf), and the late embryonic stage (5 dpf) dataset represented the third group. This suggests that at least two major transcriptional regulation changes exist. The first at the beginning of the early gastrulation, and a second one when the embryos hatch.

Gene Ontology Analysis

To further investigate the transcriptional changes during development, I decided to per- form a more detailed analysis of the changes of the expression over time. I aimed at detecting transcription factors that showed a similar pattern in their expression over time. Furthermore, I wanted to know which patterns (profiles) are the most common ones. With the help of the program STEM (Ernst and Bar-Joseph 2006), I could detect 11 signifi- cantly enriched profiles (Chapter 5.3.6). To further evaluate the profiles, I performed a Gene Ontology analysis with the genes associated with each profile. The results are pre- sented in Table 6.11 and Appendix B. Profiles having the highest expression (peak) at the 2-cell and 30% epiboly stage were related with gastrulation and protein metabolism. Tay

et al.2006 showed also a peak in protein expression at around 6 hpf. Profiles describing a

similar expression over the whole development (Profile 49 and 48) were linked with organ development. The profiles that peaked at the 5 dpf stage were enriched in nervous system development and biosynthesis according to the GO analysis.

Time Depended Biomarker Genes

Based on the results of the developmental stages in the transcription factor study, I defined a set of biomarker genes that are specific for each of the six developmental stages used. 289 transcription factors were expressed only in one stage. These biomarker genes can be used to identify, for example, developmental delays in compound exposure experiments. The number of specific transcripts for each stage can be found in Table 6.12.

The early 2-cell and the late 5dpf stage showed the highest amount of specifically expressed transcription factors. Due to the size of the list, it is only included on the supplementary CD.

In order to detect whether certain treatments caused a developmental delay, I used the 24 hpf biomarker gene set on the 10 compound data. However, none of the genes was differentially regulated. This might be because the concentrations were chosen not to cause any phenotypic effect.

6.3.2

Tissues

I analyzed four different tissue samples. The tail sample represented a muscle rich tissue; the other three samples were whole head, representing the brain, and two specific parts

Profile 2-cell 30%-epioly 1-6 somites 24 hpf 48 hpf 5 dpf Gene Ontology terms 39 Nerv ous system de v elopment, biosynthesis, gene expression, re gulation of biological quality 43 Gastrulation, protein transport, cellular response to stimulus, cellular component mo v ement, chromosome or g ani- zation, small GTP ase mediated signal transduction, protein modification by small protein conjug ation or remo v al 45 Protein modification by small protein conjug ation or remo v al, nucleoc ytoplasmic transport, cellular metabolism, or g anelle or g anization, response to stress, small GTP ase mediated signal transduction 47 Embryo de v elopment, cell dif fereantiation, or g anelle or g anization, cellular component or g anization, protein metabolism, signal transduction 49 Or g an morphogenesis, chromatin or g anization, DN A metabolism, cellular component or g anization, cellular re- sponse to stimulus 44 Protein modification by small protein conjug ation or remo v al 48 Or g an de v elopment 18 Cellular de v elopmental process, ne g ati v e re gulation of cellular process, biosynthesis 41 Positi v e re gulation of cellular process, nuclei c acid metabolism, pattern specification process, cellular response to stimulus 38 Gastrulation, signal transduction, protein modification by small protein conjug ation or remo v al 23 Cell de v elopment , cytosk eleton or g anization , nerv ous system, cellular nitrogen compound metabolism, re gulation of cellular process, cellular component or g anization T able 6.11: Gene Ontology results from the 11 profiles. Red cells mark the peak of the profile.

6.3 TRANSCRIPTION FACTOR STUDY 115

2-cell 30% epiboly 1-6 somites 24 hpf 48 hpf 5 dpf

unique 119 40 27 6 16 81

Table 6.12: Stage specific expressed transcription factors

of the brain, the diencephalon and the telencephalon. In the cluster analysis, the head sample clustered together with the 5dpf larva stage. Interestingly, the tail sample clustered with the pre-gastrula stages. Further analysis revealed that this seems to be caused due to bone and other tissue impurities in the tail sample. The two brain tissues shared no high similarity with any of the developmental stages. They also did not show a high similarity with the head, but as expected, they had more similarity with the head than with the tail sample (Chapter 5.3.5). Interestingly, the diencephalon showed the highest amount of expressed transcription factors (2666). The other tissues were slightly bellow (head 2391, telencephanoln 2443, tail 2493) (Chapter 5.3.4). Based on the results of the microarray analysis, I defined sets of biomarker genes specific for the four tissues. The lists are shown in Appendix C. Transcription factors expressed in the head sample were not excluded from being a possible biomarker gene specific for the telencephalon or the diencephalon and the other way around.

6.3.3

Conclusion

The transcription factor study should help to obtain deeper insights into the transcriptional regulation during zebrafish development. Additionally, we were also interested in the dif- ferent transcription factors expressed in muscle and brain. For this reason, I designed a new microarray consisting only of transcription factors. We performed microarrays for 6 different developmental stages and four different tissue samples. In order to be able to compare all the different datasets, I developed a new analysis method. My approach is able to detect expressed transcripts without requiring a control dataset but still makes use of both color channels. In general, around 2670 transcription factors were expressed in the different developmental samples. I could detect two major changes in the transcriptional expression pattern during the development. One at the beginning of gastrulation and a second one at around 48 hpf when the embryos hatch. I could also detect groups of tran- scription factors that exhibited a similar expression pattern over time. The Gene Ontology analysis of the patterns revealed that transcription factors with highest expression before gastrulation were mostly involved in protein metabolism. Transcription factors expressed at similar levels during the whole development period were likely involved in organ de- velopment, and transcription factors peaking at the end of the development seemed to be mostly involved in the nervous system development and biosynthesis. Based on the re- sults of the microarray analysis, I defined biomarker genes specific for the 6 developmen- tal stages used in this study. The analysis of the tissue samples revealed that expression patterns of the adult tail shared high similarity with pre-gastrula stages whereas the adult head showed a similar expression like the 5 dpf larva. Further analysis revealed that this

seems to be caused due to bone and other tissue impurities in the tail sample. In all tis- sue samples, more then 2400 transcription factors were expressed. With the help of the microarray results, I designed biomarker genes specific for diencephalon, telencephalon, whole brain (head sample), and for tail tissue (tail sample). For most of the biomarker genes, I could find evidence that they are expressed in certain tissues or stages, but in all cases, it is known that they are also expressed in other stages or tissues. The detec- tion limit of microarrays makes it quite difficult to use them for identification of specific biomarker genes. If genes are only expressed in a few cells, microarrays are not able to detect an expression signal. This means that genes need to be either highly expressed in a few cells or at moderate levels across the whole tissue or embryo. Furthermore, we used only four different tissues. Consequently, we cannot exclude the possibility that a transcription factor is expressed in any other tissue. The same applies for the biomarker genes specific for the developmental stages. The biomarker genes are not specific in the sense that they are expressed uniquely in one specific tissue or stage. They rather repre- sent transcription factors exhibiting a striking expression pattern specific for only one of the samples in our study. Since transcription factors are key players in the regulation of gene transcription, the biomarker genes identified here may still play an important role in the transcriptional regulation in their associated stage or tissue.

117

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